基质(化学分析)
计算机科学
算法
低秩近似
秩(图论)
奇异值分解
协方差矩阵
矩阵分解
矩阵范数
压缩传感
数学
矩阵完成
到达方向
稀疏矩阵
矩阵的特征分解
估计员
子空间拓扑
托普利兹矩阵
作者
Xiyan Tian,Jinhui Lei,Liufeng Du
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2018-04-04
卷期号:6: 17407-17414
被引量:4
标识
DOI:10.1109/access.2018.2820165
摘要
While the sparse methods for 1-D direction-of-arrival (DOA) estimation are extensively studied in literature, the research for 2-D DOA estimation is rare. The main reason is that, for utilizing the on-grid or off-grid sparse methods, the 2-D continuous angle space should have to be discretized, which, however, may bring unacceptable computations due to the high dimensionality of the angle space. Hence, incorporating the gridless sparse methods which require no discretization into the 2-D DOA estimation is essential. In this paper, we propose a gridless 2-D DOA estimation method based on the low-rank matrix reconstruction and the Vandermonde decomposition theorem. We first reconstruct the covariance matrix with certain structure (i.e., low-rank, Toeplitz, and positive semidefinite), and then, retrieve the DOAs by using the Vandermonde decomposition theorem. We also present a theorem to guarantee that the true DOAs can be exactly recovered in certain condition. A faster algorithmic implementation is then given by deriving the dual problem of the original one. Our proposed method is applicable for both the uniform rectangular arrays and the sparse rectangular arrays. Extensive simulations are provided to evaluate its estimation performance and the adaptability to various array geometries.
科研通智能强力驱动
Strongly Powered by AbleSci AI